Healthcare costs are skyrocketing! Reduce costs and optimize with AI

In 2016, healthcare costs in the US are estimated at nearly 18 percent of the GDP! Cost reduction is a high priority for all types of healthcare organizations, but especially healthcare providers such as hospitals, clinics, and many others. Optimizing operations within healthcare providers offers substantial benefits in both efficiency and cost reduction. In this use case, healthcare providers seek to optimize resource and asset allocation over time. This can include the allocation of medical devices, staffing of healthcare professionals, and other key aspects of operations. Efficient and affordable healthcare can help significantly improve the experience of patients, healthcare professionals, and even improve the quality of care and patient outcomes. Especially in emergency situations where the lack of healthcare resources or asset allocation could impact patient care. For healthcare professionals under time and cost reduction pressure, and increasingly at risk of clinician burnout, efficient operations and improved experience can be a significant morale booster.

Below I highlight three strategies to optimize healthcare using artificial intelligence.

Optimize your healthcare operations using AI

Artificial intelligence (AI) and machine learning (ML) have great potential to help healthcare providers identify opportunities to optimize their operations and realize cost savings. For example, AI could be used to predict a patient’s length of stay. The ability to accurately predict a patient’s length of stay can streamline operations by ensuring that the hospital has adequate, but not excessive staffing, and asset allocation for the duration of the patient stay.

In this solution, historical data is used to train ML models that can then be used to predict future length of stay. This historical data can include patient conditions such as asthma, pneumonia, malnutrition, etc. It can also include patient vital measurements such as blood pressure, pulse, and respiration. Lastly, each record includes the actual patient length of stay. Generally, the more training data the better the quality of inference and the lower the error margins. Also ensuring high quality training data will improve inference results. Conversely, training data that is biased can affect the ML model and cause wider error margins. For example, measurements from a faulty sensor such as a faulty blood pressure cuff can be biased, and training AI models with such data can lead to AI models that are also biased. Typically to ensure a reasonable quality of inference requires at least 100,000 records or a smaller number of high-quality records.

Deploy AI in the cloud to further reduce costs, improve agility, and scalability

Maintaining IT data center computing equipment in a hospital or other healthcare provider facilities requires substantial capital, as well as expensive IT and cybersecurity resources to maintain and secure it. That capital could be allocated more directly to improving patient care. For example, investing in new kidney dialysis machines. In contrast deploying to the cloud avoids much of the expense of data center equipment, and substantially reduces the requirements for IT and cybersecurity resources. Current IT resources can then devote more time to other activities related to AI or ML. Cloud based deployments are also much more agile and scalable, able to rapidly adapt to changing healthcare requirements and grow as needed.

Accelerate your AI cloud initiative with blueprints

As the saying goes, the future is already here, it’s just not evenly distributed. AI is a transformational technology and a big part of the future. The key is to get started. You could start your healthcare optimization from scratch with an AI initiative. Or you could accelerate your initiative using Microsoft healthcare AI blueprints. These blueprints include example code, test data, automated deployment, and much more that enable you to rapidly establish an initial working reference deployment that you can study and then customize to meet your requirements. A blueprint can get you 50-90 percent closer towards your end solution, versus starting from zero. The health data and AI blueprint is an example of such a blueprint. It is also focused on the patient length of stay prediction use case discussed above.

Collaboration

What other opportunities are you seeing to optimize healthcare and reduce costs with AI? We welcome any feedback or questions you may have below in the comments. AI in healthcare is a fast-moving field. New developments are emerging daily. Many of these new developments are not things you can read about yet in a textbook. I post daily about these new developments and solutions in healthcare, AI, cloud computing, security, privacy, and compliance on social media. Reach out to connect with me on LinkedIn and Twitter.